Albert Clapes, Miguel Reyes, & Sergio Escalera. (2012). User Identification and Object Recognition in Clutter Scenes Based on RGB-Depth Analysis. In 7th Conference on Articulated Motion and Deformable Objects (Vol. 7378, pp. 1–11). LNCS. Springer Berlin Heidelberg.
Abstract: We propose an automatic system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized online using robust statistical approaches over RGBD descriptions. Finally, the system saves the historic of user-object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches.
|
Wenjuan Gong, Jordi Gonzalez, Joao Manuel R. S. Taveres, & Xavier Roca. (2012). A New Image Dataset on Human Interactions. In 7th Conference on Articulated Motion and Deformable Objects (Vol. 7378, pp. 204–209). Springer Berlin Heidelberg.
Abstract: This article describes a new collection of still image dataset which are dedicated to interactions between people. Human action recognition from still images have been a hot topic recently, but most of them are actions performed by a single person, like running, walking, riding bikes, phoning and so on and there is no interactions between people in one image. The dataset collected in this paper are concentrating on human interaction between two people aiming to explore this new topic in the research area of action recognition from still images.
|
Sergio Escalera. (2012). Human Behavior Analysis From Depth Maps. In F.J. Perales, R.B. Fisher, & T.B. Moeslund (Eds.), 7th Conference on Articulated Motion and Deformable Objects (Vol. 7378, pp. 282–292). Springer Heidelberg.
Abstract: Pose Recovery (PR) and Human Behavior Analysis (HBA) have been a main focus of interest from the beginnings of Computer Vision and Machine Learning. PR and HBA were originally addressed by the analysis of still images and image sequences. More recent strategies consisted of Motion Capture technology (MOCAP), based on the synchronization of multiple cameras in controlled environments; and the analysis of depth maps from Time-of-Flight (ToF) technology, based on range image recording from distance sensor measurements. Recently, with the appearance of the multi-modal RGBD information provided by the low cost Kinect \textsfTM sensor (from RGB and Depth, respectively), classical methods for PR and HBA have been redefined, and new strategies have been proposed. In this paper, the recent contributions and future trends of multi-modal RGBD data analysis for PR and HBA are reviewed and discussed.
|
Carles Sanchez, F. Javier Sanchez, Antoni Rosell, & Debora Gil. (2012). An illumination model of the trachea appearance in videobronchoscopy images. In Image Analysis and Recognition (Vol. 7325, pp. 313–320). LNCS. Springer Berlin Heidelberg.
Abstract: Videobronchoscopy is a medical imaging technique that allows interactive navigation inside the respiratory pathways. This imaging modality provides realistic images and allows non-invasive minimal intervention procedures. Tracheal procedures are routinary interventions that require assessment of the percentage of obstructed pathway for injury (stenosis) detection. Visual assessment in videobronchoscopic sequences requires high expertise of trachea anatomy and is prone to human error.
This paper introduces an automatic method for the estimation of steneosed trachea percentage reduction in videobronchoscopic images. We look for tracheal rings , whose deformation determines the degree of obstruction. For ring extraction , we present a ring detector based on an illumination and appearance model. This model allows us to parametrise the ring detection. Finally, we can infer optimal estimation parameters for any video resolution.
Keywords: Bronchoscopy, tracheal ring, stenosis assesment, trachea appearance model, segmentation
|
Ricard Borras, Agata Lapedriza, & Laura Igual. (2012). Depth Information in Human Gait Analysis: An Experimental Study on Gender Recognition. In 9th International Conference on Image Analysis and Recognition (Vol. 7325, pp. 98–105). Springer Berlin Heidelberg.
Abstract: This work presents DGait, a new gait database acquired with a depth camera. This database contains videos from 53 subjects walking in different directions. The intent of this database is to provide a public set to explore whether the depth can be used as an additional information source for gait classification purposes. Each video is labelled according to subject, gender and age. Furthermore, for each subject and view point, we provide initial and final frames of an entire walk cycle. On the other hand, we perform gait-based gender classification experiments with DGait database, in order to illustrate the usefulness of depth information for this purpose. In our experiments, we extract 2D and 3D gait features based on shape descriptors, and compare the performance of these features for gender identification, using a Kernel SVM. The obtained results show that depth can be an information source of great relevance for gait classification problems.
|
Laura Igual, Joan Carles Soliva, Roger Gimeno, Sergio Escalera, Oscar Vilarroya, & Petia Radeva. (2012). Automatic Internal Segmentation of Caudate Nucleus for Diagnosis of Attention Deficit Hyperactivity Disorder. In 9th International Conference on Image Analysis and Recognition (Vol. 7325, pp. 222–229). LNCS.
Abstract: Poster
Studies on volumetric brain Magnetic Resonance Imaging (MRI) showed neuroanatomical abnormalities in pediatric Attention-Deficit/Hyperactivity Disorder (ADHD). In particular, the diminished right caudate volume is one of the most replicated findings among ADHD samples in morphometric MRI studies. In this paper, we propose a fully-automatic method for internal caudate nucleus segmentation based on machine learning. Moreover, the ratio between right caudate body volume and the bilateral caudate body volume is applied in a ADHD diagnostic test. We separately validate the automatic internal segmentation of caudate in head and body structures and the diagnostic test using real data from ADHD and control subjects. As a result, we show accurate internal caudate segmentation and similar performance among the proposed automatic diagnostic test and the manual annotation.
|
Patricia Marquez, Debora Gil, & Aura Hernandez-Sabate. (2012). Error Analysis for Lucas-Kanade Based Schemes. In 9th International Conference on Image Analysis and Recognition (Vol. 7324, pp. 184–191). LNCS. Springer-Verlag Berlin Heidelberg.
Abstract: Optical flow is a valuable tool for motion analysis in medical imaging sequences. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in medical sequences. This paper presents an error analysis of Lucas-Kanade schemes in terms of intrinsic design errors and numerical stability of the algorithm. Our analysis provides a confidence measure that is naturally correlated to the accuracy of the flow field. Our experiments show the higher predictive value of our confidence measure compared to existing measures.
Keywords: Optical flow, Confidence measure, Lucas-Kanade, Cardiac Magnetic Resonance
|
Fernando Barrera, Felipe Lumbreras, & Angel Sappa. (2012). Evaluation of Similarity Functions in Multimodal Stereo. In 9th International Conference on Image Analysis and Recognition (Vol. 7324, pp. 320–329). LNCS. Springer Berlin Heidelberg.
Abstract: This paper presents an evaluation framework for multimodal stereo matching, which allows to compare the performance of four similarity functions. Additionally, it presents details of a multimodal stereo head that supply thermal infrared and color images, as well as, aspects of its calibration and rectification. The pipeline includes a novel method for the disparity selection, which is suitable for evaluating the similarity functions. Finally, a benchmark for comparing different initializations of the proposed framework is presented. Similarity functions are based on mutual information, gradient orientation and scale space representations. Their evaluation is performed using two metrics: i) disparity error, and ii) number of correct matches on planar regions. In addition to the proposed evaluation, the current paper also shows that 3D sparse representations can be recovered from such a multimodal stereo head.
Keywords: Aveiro, Portugal
|
Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2012). Efficient pairwise classification using Local Cross Off strategy. In 25th Canadian Conference on Artificial Intelligence (Vol. 7310, pp. 25–36). LNCS.
Abstract: The pairwise classification approach tends to perform better than other well-known approaches when dealing with multiclass classification problems. In the pairwise approach, however, the nuisance votes of many irrelevant classifiers may result in a wrong prediction class. To overcome this problem, a novel method, Local Crossing Off (LCO), is presented and evaluated in this paper. The proposed LCO system takes advantage of nearest neighbor classification algorithm because of its simplicity and speed, as well as the strength of other two powerful binary classifiers to discriminate between two classes. This paper provides a set of experimental results on 20 datasets using two base learners: Neural Networks and Support Vector Machines. The results show that the proposed technique not only achieves better classification accuracy, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.
|
Sergio Vera, Debora Gil, Agnes Borras, F. Javier Sanchez, Frederic Perez, Marius G. Linguraru, et al. (2012). Computation and Evaluation of Medial Surfaces for Shape Representation of Abdominal Organs. In H. Yoshida et al (Ed.), Workshop on Computational and Clinical Applications in Abdominal Imaging (Vol. 7029, 223–230). LNCS. Berlin: Springer Link.
Abstract: Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D
objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial
manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our
method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs,
exploring the use of medial manifolds for the representation of multi-organ relations.
Keywords: medial manifolds, abdomen.
|
Bogdan Raducanu, & Fadi Dornaika. (2011). A Discriminative Non-Linear Manifold Learning Technique for Face Recognition. In Informatics Engineering and Information Science (Vol. 254, pp. 339–353). Springer Berlin Heidelberg.
Abstract: In this paper we propose a novel non-linear discriminative analysis technique for manifold learning. The proposed approach is a discriminant version of Laplacian Eigenmaps which takes into account the class label information in order to guide the procedure of non-linear dimensionality reduction. By following the large margin concept, the graph Laplacian is split in two components: within-class graph and between-class graph to better characterize the discriminant property of the data.
Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques. The experimental results confirm that our method outperforms, in general, the existing ones. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variance in their appearance.
|
Angel Sappa, David Geronimo, Fadi Dornaika, Mohammad Rouhani, & Antonio Lopez. (2012). Moving object detection from mobile platforms using stereo data registration. In Marek R. Ogiela, & Lakhmi C. Jain (Eds.), Computational Intelligence paradigms in advanced pattern classification (Vol. 386, pp. 25–37). Springer Berlin Heidelberg.
Abstract: This chapter describes a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three main stages. Initially, feature points are extracted and tracked through consecutive 2D frames. Then, a RANSAC based approach is used for registering two point sets, with known correspondences in the 3D space. The computed 3D rigid displacement is used to map two consecutive 3D point clouds into the same coordinate system by means of the quaternion method. Finally, moving objects correspond to those areas with large 3D registration errors. Experimental results show the viability of the proposed approach to detect moving objects like vehicles or pedestrians in different urban scenarios.
Keywords: pedestrian detection
|
Pau Baiget, Carles Fernandez, Xavier Roca, & Jordi Gonzalez. (2012). Trajectory-Based Abnormality Categorization for Learning Route Patterns in Surveillance. In Detection and Identification of Rare Audiovisual Cues, Studies in Computational Intelligence (Vol. 384, pp. 87–95). Springer Berlin Heidelberg.
Abstract: The recognition of abnormal behaviors in video sequences has raised as a hot topic in video understanding research. Particularly, an important challenge resides on automatically detecting abnormality. However, there is no convention about the types of anomalies that training data should derive. In surveillance, these are typically detected when new observations differ substantially from observed, previously learned behavior models, which represent normality. This paper focuses on properly defining anomalies within trajectory analysis: we propose a hierarchical representation conformed by Soft, Intermediate, and Hard Anomaly, which are identified from the extent and nature of deviation from learned models. Towards this end, a novel Gaussian Mixture Model representation of learned route patterns creates a probabilistic map of the image plane, which is applied to detect and classify anomalies in real-time. Our method overcomes limitations of similar existing approaches, and performs correctly even when the tracking is affected by different sources of noise. The reliability of our approach is demonstrated experimentally.
|
Muhammad Anwer Rao, David Vazquez, & Antonio Lopez. (2011). Color Contribution to Part-Based Person Detection in Different Types of Scenarios. In W. Kropatsch A. Berciano H. Molina D. D. P. Real (Ed.), 14th International Conference on Computer Analysis of Images and Patterns (Vol. 6855, pp. 463–470). Berlin Heidelberg: Springer.
Abstract: Camera-based person detection is of paramount interest due to its potential applications. The task is diffcult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al. These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al. appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted suficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system.We will compute the HOG on top of OPP, then we train and test the part-based human classifer by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the beneficts of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution.
Keywords: Pedestrian Detection; Color
|
Naveen Onkarappa, & Angel Sappa. (2011). Space Variant Representations for Mobile Platform Vision Applications. In W. Kropatsch A. Berciano H. Molina D. D. P. Real (Ed.), 14th International Conference on Computer Analysis of Images and Patterns (Vol. 6855, pp. 146–154). Springer Berlin Heidelberg.
Abstract: The log-polar space variant representation, motivated by biological vision, has been widely studied in the literature. Its data reduction and invariance properties made it useful in many vision applications. However, due to its nature, it fails in preserving features in the periphery. In the current work, as an attempt to overcome this problem, we propose a novel space-variant representation. It is evaluated and proved to be better than the log-polar representation in preserving the peripheral information, crucial for on-board mobile vision applications. The evaluation is performed by comparing log-polar and the proposed representation once they are used for estimating dense optical flow.
|
